Dentistry is no longer hesitating on the sidelines with AI. Dental service organizations (DSOs) and larger group practices are moving full steam ahead into broader AI integration, while many independent practices are adopting AI one software update, vendor suggestion, or staff workaround at a time.
AI-powered tools are scheduling, getting insurance verification, processing claims and denials workflows, and working as scribes, voice agents, and within patient responses. For practices facing staffing shortages, payer challenges, and inconsistent follow-up, the appeal of these efficiencies is obvious.
Jacob Nolen, MBA.
The risk is that AI is entering dental operations faster than many practices are able to govern it. The biggest exposure is not the AI tool that leadership approved after a formal review and pilot process.
Rather, it comes from the capability that slipped through an existing software platform: the transcription app a staff member decided to try out on a personal phone, the patient note pasted into a consumer chatbot, or the claims tool that brought AI into the mix before anyone mapped where the data actually went.
That is shadow AI, and it is becoming one of the most significant unmanaged patient data risks in dentistry. In dental settings, shadow AI usually looks like an ordinary workflow: AI-supported scheduling, automated patient messages, claims support, imaging assistance, call summaries, or a free tool used for wordsmithing patient instructions.
Each use seems reasonable enough on its own. But together, they raise a larger question that many practices are finding they can’t answer clearly: Where is AI touching our protected health information (PHI)?
For some sensitive workflows, that question also points to the value of on-premises or otherwise controlled AI environments. When clinical imaging, documentation, or high-volume PHI processing stays inside an environment the organization can govern, practices reduce unnecessary data movement and limit the number of outside processors involved.
Most dental organizations know the major platforms they use. But fewer have a current data flow map showing where PHI leaves the practice, which vendors and subprocessors touch it, where information is processed and stored, whether patient data are used to train or improve models, and how long that data are retained.
For example, a practice owner or DSO executive believes they have a single vendor relationship. The reality is that one AI-supported workflow might involve the primary software vendor plus the cloud hosting provider, a model provider, a storage layer, and additional subcontractors. If clinical notes, images, insurance details, or patient identifiers move through that chain, the practice must be able to track them.
Many dental organizations assume the practice management vendor, imaging vendor, or patient communication vendor are handling security and compliance on their behalf. But vendors are responsible for their own systems.
Dental practices remain responsible for understanding how patient data are handled inside the AI workflows they use. HIPAA responsibilities do not end with a signed contract if the actual data-handling practices are unclear. A signed business agreement does not replace vendor oversight, staff policy, technical review, data retention controls, or a current list of subcontractors and subprocessors.
The challenge, of course, grows with the size of an organization. For DSOs and multilocation groups, their scale multiplies every weak spot, compounding issues with more vendor integrations, cloud systems, legacy platforms, and inherited technology from acquisitions. In a 50-location group, a shallow cursory review of AI integration could create an expensive, multilocation compliance issue involving breach notification, operational disruption, and patient trust.
AI has concrete value in dentistry, but the value depends on accurate data, intentional workflows, human oversight, and strict rules for how patient information is handled. And it’s important to distinguish clinical AI from lower-risk operational AI.
Scheduling reminders, recall campaigns, and general marketing workflows work well with vetted cloud tools under the right agreements and controls. On the other hand, clinical imaging, high-volume PHI processing, sensitive documentation, and detailed patient records require deeper scrutiny and process design.
This is where infrastructure becomes part of governance rather than a separate information technology decision. Dental leaders need to understand how many systems, credentials, processors, and vendors sit between the patient record and the AI output. That is why system visibility needs to come before aggressive AI expansion.
Dental leaders should start with a practical inventory of where AI is already operating. That includes the obvious areas like imaging AI and scheduling automation along with less visible uses such as call transcription, email drafting, patient message generation, claims support, analytics, marketing tools, and staff-introduced productivity applications.
The inventory should answer basic questions, such as:
- Which AI tools are active?
- Which ones touch PHI?
- Which were approved, and which came through the back door of vendor updates or staff workarounds?
- What data does each tool process, and where is that data stored?
- Do vendors and subprocessors have access?
- Are audit logs available and transparent?
- What happens to the data when the contract ends?
Dentistry will benefit from AI when practices know where the technology operates, where patient data move, and who is responsible for protecting it. While controlled infrastructure will not be necessary for every AI use case, it becomes more relevant when the workload involves continuous PHI, clinical imaging, or data that should not be moving through a long chain of outside processors.
Practices that answer these questions now will be better positioned to confidently reap the efficiency and cost savings of AI. Those who wait could find themselves trying to explain patient data exposure they did not even realize was happening.
Jacob Nolen, MBA, has a background in enterprise consulting and financial services. Prior to joining Go Abacus, he served as associate director at Clarity Family Office, advising entrepreneurial families on wealth and business strategy, and as a senior associate at Kalicki Collier, a firm specializing in estate planning, trust administration, and tax planning. Nolen drives the commercial revenue strategy for Go Abacus and leads a growing sales organization focused on bringing AI to institutions where data sovereignty and regulatory compliance are crucial.
The comments and observations expressed herein do not necessarily reflect the opinions of DrBicuspid.com, nor should they be construed as an endorsement or admonishment of any particular idea, vendor, or organization.
